During tumorigenesis, cancer cells as well as healthy cells depend upon growth factors produced in an autocrine and paracrine manner to maintain their proliferation and differentiation. Hence there exists a tug-of war for growth factors between a tumor and the surrounding non-cancer cells, and the fitness of each individual cell must be defined by this competition at the population level. Importantly, cancer cells on the leading edge of tumors are characterized as being chemo-resistant, have enhanced metastatic potential and are extremely efficient at forming new tumors [6]. We have determined that cancer cells and adjacent stromal cells express high levels of the growth factor IL-6 and activated Stat3 which are hypothesized to be the principal mediators of both tumorigenesis and metastatic progression. In this proposal, we focus on the IL-6/pStat3 pathway in melanoma and breast cancer cells, whose proliferation and differentiation critically depends on pStat3 signals. In the context of understanding tumor dynamics, especially during drug treatment, the intraclonal competition for growth factors within a genetically-idenfical population of cells will be investigated. An understanding of the resulting phenotypic diversity of tumor cells will lead to improved therapeutic interventions eradicating tumor populations. Specifically, we will (1) characterize the variable response of tumor cells to growth factors and targeted inhibitors: (2) design a mathematical framework to predict the consequences of cellular diversity and identify the optimum therapeutic intervention that maximizes the chance of eradicating the tumor; and (3) validate the predictions of the mathematical framework in cell lines and murine models. This project fully leverages our expertise in cancer biology and clinical experience (Bromberg), single cell profiling and biochemical modeling (Altan-Bonnet), and mathematical modeling (Michor). We will dissect how the IL-6 pathway can generate phenotypic variability in tumors, which drives their progression and causes resistance to targeted therapies [7- 13]. Based on our models, we will identify and test the optimal therapeutic protocol for treating these cancers.